Like many others, I have been very influenced by Bob, and I owe him a lot personally as well. Bob pretty much handed me the basic idea for a "Random walk in GNP" on a silver platter. Bob's review of a report to the OECD, which he might rather forget, inspired the Grumpy Economist many years later. Bob is a straight-arrow icon for how academics should conduct themselves.

This is a great economics paper in the Bob Lucas tradition: Preferences, technology, equilibrium, predictions, facts, welfare calculations, full stop.

However, it’s not yet a great finance paper. It’s missing the motivation, vision, methodological speculation, calls for future research — in short, all the BS — that Bob tells you to leave out. I’ll follow my comparative advantage, then, to help to fill this yawning gap.

Volume is The Great Unsolved Problem of Financial Economics. In our canonical models — such as Bob’s classic consumption-based model — trading volume is essentially zero.

The reason is beautifully set out in Nancy Stokey and Paul Milgrom’s no-trade theorem, which I call the Groucho Marx theorem: don’t belong to any club that will have you as a member. If someone offers to sell you something, he knows something you don’t.

More deeply, all trading — any deviation of portfolios from the value-weighted market index — is zero sum. Informed traders do not make money from us passive investors, they make money from other traders.

It is not a puzzle that informed traders trade and make money. The deep puzzle is why the uninformed trade, when they could do better by indexing.

Here’s how markets “should” work: You think the new iPhone is great. You try to buy Apple stock, but you run in to a wall of indexers. “How about $100?” “Sorry, we only buy and sell the whole index.” “Well, how about $120?” “Are you deaf?” You keep trying until you bid the price up to the efficient-market value, but no shares trade hands.

And, soon, seeing the futility of the whole business, nobody serves on committees any more. Why put time and effort into finding information if you can’t profit from it? If information is expensive to obtain, then nobody bothers, and markets cannot become efficient. (This is the Grossman-Stiglitz theorem on the impossibility of efficient markets.)

I gather quantum mechanics is off by 10 to the 120th power in the mass of empty space, which determines the fate of the universe. Volume is a puzzle of the same order, and importance, at least within our little universe.

Stock exchanges exist to support information trading. The theory of finance predicts that stock exchanges, the central institution it studies, the central source of our data, should not exist. The tiny amounts of trading you can generate for life cycle or other reasons could all easily be handled at a bank. All of the smart students I sent to Wall Street for 20 years went to participate in something that my theory said should not exist.

And it’s an important puzzle. For a long time, I think, finance got by on the presumption that we’ll get the price mostly right with the zero-volume theory, and you microstructure guys can have the last 10 basis points. More recent empirical work makes that guess seem quite wrong. It turns out to be true that prices rise when a lot of people place buy orders, despite the fact that there is a seller for each buyer. There is a strong correlation between the level of prices and trading volume — price booms involve huge turnover, busts are quiet.

At a deeper level, if we need trading to make prices efficient, but we have no idea how that process works, we are in danger that prices are quite far from efficient. Perhaps there is too little trading volume, as the rewards for digging up information are not high enough! (Ken French’s AFA presidential speech artfully asks this question.)

Our policy makers, as well as far too many economists, jump from not understanding something, to that something must be wrong, irrational, exploitative, or reflective of “greed” and needs to be stopped. A large transactions tax could well be imposed soon. Half of Washington and most of Harvard believes there is “too much” finance, meaning trading, not compliance staff, and needs policy interventions to cut trading down. The SEC and CFTC already regulate trading in great detail, and send people to jail for helping to incorporate information in to prices in ways they disapprove of. Without a good model of information trading those judgments are guesses, but equally hard to refute.

How do we get out of this conundrum? Well, so far, by a sequence of ugly patches.

Grossman and Stiglitz added “noise traders.” Why they trade rather than index is just outside the model.

Another strand, for example Viral Acharya and Lasse Pedersen’s liquidity based asset pricing model, uses life cycle motives, what you here would recognize as an overlapping generations model. They imagine that people work a week, retire for a week, and die without descendants. Well, that gets them to trade. But people are not fruit flies either.

Fernando and Andy adopt another common trick — unobservable preference shocks. If trade fundamentally comes from preferences rather than information then we avoid the puzzle of who signs up to lose money.

I don’t think it does a lot of good to call them shocks to risk aversion, and tie them to habit formation, as enamored as I am of that formulation in other contexts. Habit formation induces changes in risk aversion from changes in consumption. That makes risk aversion shocks observable, and hence contractable, which would undo trading.

More deeply, to explain volume in individual securities, you need a shock that makes you more risk averse to Apple and less risk averse to Google. It can be done, but it is less attractive and pretty close to preferences for shares themselves.

Finally, trading is huge, and hugely concentrated. Renaissance seems to have a preference shock every 10 milliseconds. I last rebalanced in 1994.

The key first principle of modern finance, going back to Markowitz, is that preferences attach to money — to the payoffs of portfolios — not to the securities that make up portfolios. A basket of stocks is not a basket of fruits. It’s not the first time that researchers have crossed this bright line. Fama and French do it. But if it is a necessary condition to generate volume, it’s awfully unpalatable. Do we really need to throw out this most basic insight of modern finance?

Another strain of literature supposes people have “dogmatic priors” or suffer from “overconfidence.” (José Scheinkman and Wei Xiong have a very nice paper along these lines, echoing Harrison and Kerps much earlier.) Perhaps. I ask practitioners why they trade and they say “I’m smarter than the average.” Exactly half are mistaken.

At one level this is a plausible path. It takes just a little overconfidence in one’s own signal to undo the no-trade-theorem information story — to introduce a little doubt into the “if he’s offering to sell me something he knows something I don’t” recursion.

On the other hand, understanding that other people are just like us, and therefore inferring motives behind actions, is very deep in psychology and rationality as well. Even chimps, offered to trade a banana for an apple, will check to make sure the banana isn’t rotten.

(Disclaimer: I made the banana story up. I remember seeing a science show on PBS about how chimps and other mammals that pass the dot test have a theory of mind, understand that others are like them and therefore question motives. But I don’t have the reference handy. Update: A friend sends this and this.)

More deeply, if you are forced to trade, a little overconfidence will get it going. But why trade at all? Why not index and make sure you’re not one of the losers? Inferring information from other’s offer to trade is only half of the no-trade theorem. The fact that rational people don’t enter a zero-sum casino in the first place is the other, much more robust, half. That line of thought equates trading with gambling — also a puzzle — or other fundamentally irrational behavior.

But are we really satisfied to state that the existence of exchanges, and the fact that information percolates into prices via a series of trades, are facts only “explainable" by human folly, that would be absent in a more perfect (or perfectly-run) world?

Moreover, that “people are idiots” (what Owen Lamont once humorously called a “technical term of behavioral finance”) might be a trenchant observation on the human condition. But, by being capable of “explaining” everything, it is not a theory of anything, as Bob Lucas uses the word “theory.”

The sheer volume of trading is the puzzle. All these non-information mechanisms — life-cycle, preference shocks, rebalancing among heterogeneous agents (Andy Lo and Jiang Wang), preference shifts, generate trading volume. But they do not generate the astronomical magnitude and concentration of volume that we see.

We know what this huge volume of trading is about. It’s about information, not preference shocks. Information seems to need trades to percolate into prices. We just don’t understand why.

Does this matter? How realistic do micro foundations have to be anyway? Actually, for Andy and Fernando’s main purpose, and that of the whole literature I just seemed to make fun of, I don’t think it’s much of a problem at all.

Grossman and Stiglitz, and their followers, want to study information traders, liquidity providers, bid-ask spreads, and other microstructure issues. Noise traders, “overconfidence,” short life spans, or preference shocks just get around the technicalities of the no-trade theorem to focus on the important part of the model, and the phenomena in the data it wants to match. Andy and Fernando want a model that generates the correlations between risk premiums and volume. For that purpose, the ultimate source of volume and why some people don’t index is probably unimportant.

We do this all the time. Bob’s great 1972 paper put people on islands and money in their hands via overlapping generations. People live in suburbs and hold money as a transactions inventory. OLG models miss velocity by a factor of 100 too. (OLG money and life-cycle volume models are closely related.) So what? Economic models are quantitative parables. You get nowhere if you fuss too much about micro foundations of peripheral parts. More precisely, we have experience and intuition that roughly the same results come from different peripheral micro foundations.

If I were trying to come up with a model of trading tomorrow, for example to address the correlation of prices with volume (my “Money as stock” left that hanging, and I’ve always wanted to come back to it), that’s what I’d do too.

At least, for positive purposes. We also have experience that models with different micro foundations can produce much the same positive predictions, but have wildly different welfare implications and policy conclusions. So I would be much more wary of policy conclusions from a model in which trading has nothing to do with information. So, though I love this paper’s answer (transactions taxes are highly damaging), and I tend to like models that produce this result, that is no more honest than most transactions tax thought, which is also an answer eternally in search of a question.

At this point, I should summarize the actual contributions of the paper. It’s really a great paper about risk sharing in incomplete markets, and less about volume. Though the micro foundations are a bit artificial, it very nicely gets at why volume factors seem to generate risk premiums. For that purpose, I agree, just why people trade so much is probably irrelevant. But, having blabbed so much about big picture, I’ll have to cut short the substance.

How will we really solve the volume puzzle, and related just what “liquidity” means? How does information make its way into markets via trading? With many PhD students in the audience, let me emphasize how deep and important this question is, and offer some wild speculations.

As in all science, new observations drive new theory. We’re learning a lot about how information gets incorporated in prices via trading. For example, Brian Weller and Shrihari Santosh show how pieces of information end up in prices through a string of intermediaries, just as vegetables make their way from farmer to your table — and with just as much objection from bien-pensant economists who have decried “profiteers” and “middlemen” for centuries.

Also, there is a lot of trading after a discrete piece of information hits the market symmetrically, such as a change in Federal Funds rate. Apparently it takes trading for people to figure out what the information means. I find this observation particularly interesting. It’s not just my signal and your signal.

And new theory demands new technique too, something that we learned from Bob. (Bob once confessed that learning the math behind dynamic programming had been really hard.)

What is this “information” anyway? Models specify a “signal” about liquidating dividends. But 99% of “information” trading is not about that at all. If you ask a high speed trader about signals about liquidating dividends, they will give you a blank stare. 99% of what they do is exactly inferring information from prices — not just the level of the price but its history, the history of quotes, volumes, and other data. This is the mechanism we need to understand.

Behind the no-trade theorem lies a classic view of information — there are 52 cards in the deck, you have three up and two down, I infer probabilities, and so forth. Omega, F, P. But when we think about information trading in asset markets, we don’t even know what the card deck is. Perhaps the ambiguity or robust control ideas Lars Hansen and Tom Sargent describe, or the descriptions of decision making under information overload that computer scientists study will hold the key. For a puzzle this big, and this intractable, I think we will end up needing new models of information itself. And then, hopefully, we will not have to throw out rationality, the implication that trading is all due to human folly, or the basic principles of finance such as preferences for money not securities.

Well, I think I’ve hit 4 of the 6 Bob Lucas deadly sins — big picture motivation, comments about about whole classes of theories, methodological musings, and wild speculation about future research. I’ll leave the last two — speculations about policy and politics, and the story of how one thought about the paper — for Andy and Fernando!

Should people read up on this issue? Yes. Should they read this? No. Is it superb? Definitely not. I suspect you might benefit from reading Larry Harris's book (undergrad/MBA level) before diving in to Hasbrouck's book (masters/PhD level). These issues get far better coverage in those books.

'Information seems to need trades to percolate into prices. We just don’t understand why.'

'How does information make its way into markets via trading?'

'Apparently it takes trading for people to figure out what the information means.'

As a non-economist, I find this subject very peculiar in that it is apparently puzzling...

A price is only real once it's cashed out, and deal isn't a done until the money changes hands. What reason do I have to believe that you will really buy/sell for $50 until I've seen cash on the table and a handshake extended?

No trades, no trust. Beliefs are cheap. Actions demonstrate commitment and are therefore the real information content.

As for the 2nd half of the theorem:

'But why trade at all? Why not index and make sure you’re not one of the losers?'

Why does anyone believe a market is ever perfectly efficient? To what degree is a price perturbation dampened and independent?It seems obvious to me any imbalance or random spike will create ripples in the pond for quite a while. And in this world the wind always blows...

I pay two types of transactions "tax" already: (1) I pay commissions to my broker; and (2) I pay capital gains tax if I sell a winner in my taxable account.

Commissions have fallen over time. The trading volume response to falling commissions might give a way to estimate the impact of a transaction tax.

Quare: If I had an account at Vanguard and transferred money from one mutual fund to another within the Vanguard family, and Vanguard handled the whole thing internally without going to the market, would that be a financial transaction subject to a financial transactions tax?

" I ask practitioners why they trade and they say “I’m smarter than the average.” Exactly half are mistaken."

Not really true. Markets are money-weighted, so half of the money traded is mistaken. Because winners have more money, and the resulting consolidation in the investment industry, most traders are mistaken.

So, they may be correct that they are "smarter than the average" when the average is the average adult, or even your average investor. Just not a dollar-weighted average of investing activity.

John, As Lucas influenced you, your work influenced the way I managed risk.I was a market maker on the CBOE and Amex, trading the S&P 500 index. Later I managed a small hedge fund. We assumed there are no informational asymmetries, hence the market was a drifting random walk. We did know that volatility, risk, varied with time and we exploited that information with time decaying derivative positions well enough to generate risk adjusted returns. Our Most profitable returns, absolute and relative occurred with market collapses. As for volume, we also observed returns were lower on higher volume.

This reminds me of an old SNL news skit. News reporters used to say: "... and today X many shares changed hands at the NYSE" The SNL reporter said, "... and no shares changed hands today at the NYSE. Finally, everyone's happy with what they've got"

I think there's too much emphasis on information as being the main driver of trading volume. I think it's mainly driven by liquidity, and it makes sense for it to be concentrated amongst a small number of liquidity providers given the economies of scale. Information trading can trigger a cascade of liquidity trades. I get some information about a stock and withdraw money from a fund to fund the purchase. The fund has to sell shares to match the redemption. Perhaps, I have to re-balance as a result of the new transaction. These actions can cause prices to move, which could trigger further trades.

Could it be that (successful) computational trading schemes are simply modeling, and taking advantage of, the psychological perversity of human traders? Or even modeling, and taking advantage of, the particular deficiencies of all the other computer programs that are trading? Volume, then, could be driven by the rate of change of biases in the humans and the computer programs. Plus the computer programs, presumably, are being frequently tweaked by humans. More so, perhaps, when they are nervous, or high on caffeine.

I do like the idea of a progressive consumption tax in place of (failed to be progressive) income tax. But wouldn't it suppress consumption and, consequently, reduce growth?

@Fish Goldstein: Not necessarily so. It could be that there are big losers, and these big losers give a little bit of money to many difefrent traders. In this scenario, way more than half the traders are winners.

We see this very clearly in online poker. If we look at it before fees (rake and/or buy-ins), some people are huge losers and way more than half are winners, albeit most of them only ever so slightly. If we add fees, only a few are winners (typically 5-20% depending on the dataset).

In both cases, suggesting that "half" are winners is surprisingly wrong.

I took "exactly half" as a literary aside ..... not to be taken literally.From the money's point of view, i.e. each individual dollar, exactly half the transactions are to a loser and half to a winner.--echofive

I've always wondered who the 'informed' traders were empirically? shouldn't there be some demographic/profession with higher-than-average returns outside of the market-makers with monopoly access to flow? Yet fund managers, sell-side analysts, everyone who seems to be 'paying for information' via their time seems to have no alpha in stock picking.

"But don't forget the big puzzle -- who are the uninformed traders who play and lose!"

Where do equity issuers fall into the "informed" vs. "uninformed" traders matrix? Obviously, General Motors isn't going to sell it's own shares to turn around and invest in an index of motor companies (Ford, Toyota, Chrysler, etc.).

It seems to me that you only think about the equity markets from the buyer's side and classify participants as either:

1. Passive buy and hold indexers2. Active traders

But I think your question "Who are the uninformed traders who play and lose?" begs another - "Are equity issuers to be considered uninformed traders since by their nature they don't index?".

That's a good guess on traders. In fact more than half of mutual fund managers trade actively and underperform the index. The question is, why do they do it, and why do we give them money? --bravobravo :)

John,"why do we give them money?"I think the reason is that we are so occupied with our own busy-ness that we end up trusting the salesman and go with what he recommends (I did).... which is what he is paid (by the fund manager's union, as it were) to recommend..... which is the the plan that benefits most the fund managers and their bosses/executives. Which gets to my usual complaint .... "corporate crap".I guess "liberals" whine and "conservatives" bleat. Not sure which one I am, if either. What do rational people do?Cheers,echofive

I'm a not an economist, so please agree to tolerate what might well be an ignorant question before reading further.....

I don't understand why we're asking "why doesn't everyone index?" Given that the expected results of indexing and trading within that index should be identical (at least before broker fees), wouldn't it be just as appropriate to ask why anyone bothers to index?

Imagine that every day I to put a series of sticky notes on my wall, each of which has the ticker symbol of a company in the S&P 500 written on its face. For every $X of market cap one of those 500 companies has, I put up another sticky note. The sticky notes are placed in random order. Next I put on a blindfold and throw 5 darts at the wall. Finally I invest 1/5th of my portfolio into each ticker symbol that I hit with a dart. Shouldn't my expected return match the index? If my expected return does match the index, then why wouldn't I trade so long as I (1) enjoy it, and (2) expect that I can at least replicate the results of a blind-folded dart throw?

Anyway, probably a silly question, but thanks in advance for any knowledge imparted.

I guess what I'm trying to ask is, putting aside the pragmatic logistics of trading (which I foolishly limited to "broker fees" in my first post: as you point out, that's just part of it), how is trading within an index any different from buying the index itself, assuming that one traded randomly and somehow accounted for weight?

My assumption was that the economic theories Grumpy is discussing are in no way premised on the average person being unable to execute trades as well as an index, it seemed more theoretical/high-level than that..... though I could certainly be wrong.

Maybe another way of asking my question: if we put aside logistical/pragmatic trading issues, aren't indexers the equivalent of those at the Roulette table who bet on "black" or "red," while traders are those who play the individual numbers in the middle of the table (the expected returns of both betting types being mathematically identical: one betting strategy is simply higher risk/reward than the other on any single spin of the wheel)? It's not a perfect analogy because, unlike Roulette, in the markets the "house numbers" go to the players rather than the casino. Hopefully you get the gist of what I'm trying to ask though.

"...aren't indexers the equivalent of those at the Roulette table who bet on black or red, while traders are those who play the individual numbers in the middle of the table..."

The roulette table is a bad analogy because the house wins in the long run generating only losses for the players in aggregate. Sure some lucky individuals will be net winners, but that will be more than offset by the numerous individuals that walk home with empty pockets.

Investing and trading in stocks is different. A growing economy generates aggregate net winners for all the players in aggregate. Sure some will suffer net losses, but those losses will be more than offset by the gains of others. Granted, losses may be concentrated in equities to the gain of bonds, land, or some other good in a growing economy, but that is a little bit beyond the scope of John's article.

I understand that concept, which is why I wrote this in my post: "It's not a perfect analogy because, unlike Roulette, in the markets the 'house numbers' go to the players rather than the casino." For the non-gambler, note that the house numbers are what gives the casino it's statistical edge in roulette.

So I guess my question is still the same..... why do economists expect people to index instead of trade? From your replies, I'm guessing that you're saying it's limited solely to an inability to trade efficiently on one's own?

how would indexing work without traders setting the prices? If everyone indexed it seems like purchases and sales would simply reflect demographics (people selling in retirement after buy/hold for decades) and not "value".

Crap like this is exactly why Chicago has had ZERO presence in recent regulatory discussions: there is a shocking level of ignorance of the entire field of market microstructure. Volume is a puzzle? Are you high? It sure as hell is NOT a puzzle. In fact, we have plenty of work on volume clustering (from Admati and Pfleiderer to Colliard, Kadan, and Kandel); work on predicting volume; numerous microstructure model where volume is an endogenous variable; work on depth (which relates to volume) by Glosten and Sandas and others; and, most interestingly, recent work on invariants by Kyle and Obizhaeva.

But you keep telling yourself it's a puzzle, John. Or maybe you could go talk to Anat, or Paul, or one of the other people at Stanford who would school you on this issue. But I've had enough interactions with you to guess that you will do no such thing and will continue to proclaim novelty where there is none.

If you read it, you will see I am aware of that work and indeed praise it. But they all take one or another shortcut to get to the interesting stuff. Is there one model in your list with fully rational agents? One that does not have liquidity traders? One that matches the astonishing size of volume, not just its interesting patterns? Much progress made -- but a basic puzzle still to go.

Well, of the works I cited only one (Admati and Pfleiderer) has liquidity traders. Johnson's work on liquidity risk; and, sequential trader models from Foucault and Parlour have endogenized volume. (Lots of others, but those are the most interesting from a volume perspective.) I typoed too: Foucault, Kadan, and Kandel. All of these only require some heterogeneity in reservation values or valuing cash.

As for the size of volume: This might just be the puzzle of market volatility versus dividend volatility. It might not seem that way, but pop those into Tim Johnson's model and you'll see they are isomorphic. Apart from that, intermediation chains are long so I doubt we'll explain volume well without models involving intermediation. Finally, the Kyle and Obizhaeva invariant work is... strange stuff, but it also seems to have potential for explaining volume.

So "puzzle" is a bit strong. We have plenty of good ideas. Volatility of prices versus dividends and interest rates? That's more of a puzzle.

If your models does not describe the real world, it's not people who are stupid, perhaps it's the models.

Trading in any modern exchange is mostly performed by robots - 60 to 80%. These robots are trained by people with slightly different backgrounds, they weigh differently the various sources of information and for this reason have differing views on the near future of markets. Moreover, even if their views are similar, these robots operate in dissimilar contexts - their actions are constrained and sometimes reversed by portfolio-wide and firm-wide risk management tools.

The remaining fraction - 20-40% - of trading is initiated by humans but is again performed by robots. Humans also have dissimilar opinions with respect to the future. Why? They have dissimilar beliefs as to what is important and what is less important. Humans speculate. Human traders also operate in dissimilar context, have differing risk tolerances which in turn are affected by their holdings which in turn are affected by their previous trading, and for this reason they have different views on long-term prospects the economy, sectors, sub-sectors, stocks, and futures, and bonds.

All these agents and principals are very much rational. Why would rational people have different opinions and value things differently? Perhaps because they have different models of the world and operate in different contexts.

Trading is a puzzle to any model that fails to account for differences in opinions (modes) and contexts and tolerances.

Is there any reference to the numbers - trading proportion performed by robots? I'm curious.

In academic community, actually, yes, "Volume is The Great Unsolved Problem of Financial Economics" as Prof. Cochrane states it clearly at the beginning, "In our canonical models — such as Bob’s classic consumption-based model — trading volume is essentially zero." I understand that in industry, we may observe many interesting patterns or generate seemingly plausible ideas. But models and theories are not just stories or strategies. Based on my own experience, it is not easy to formulate the idea and build a tractable, dynamic model to explain certain empirical facts or give rise to testable predictions.

As you write: "I ask practitioners why they trade and they say 'I’m smarter than the average'. Exactly half are mistaken." But to be a successful trader (luck aside) it is not enough to be smarter than (i.e., more knowledgeable than) the average trader: you have to be smarter than (more knowledgeable than) the market as a whole. Nowhere near half of traders meet that condition. I suspect a lot of active traders reasonably or almost reasonably think they are smarter/more-knowledgeable than average, and they don't realize that that's not good enough to justify their activity.

This is a very insightful comment and I learned a lot from the review of the relevant literature.

In emerging markets like China, a sudden pump up in trading volume typically does mean at least some informed traders have come in and an ensuing rise in price is highly likely to occur. The volume is generated by "real money". In a highly volatile market like China, traders, especially those with deep pocket,the size of the bet is based on the conviction of certain information.

"Why trade?" I asked this question to myself many times. After all, I might trade with someone in possession of information unavailable to me. Generally I became less risk-averse when I obtained or inferred information from members of top management or large shareholders supposed to be at the top tier of the information pyramid or when I had backup plans and systematic methods for managing risks.

Companies distribute their earnings using stock buybacks. Because companies conduct more buybacks when earnings are strong, they "buy-high and sell low" -only the active traders gain from that. The active traders are the counter parties who are forming the other side of the trade buying low and selling high. Passively holding a stock that has a price that bobs up and down does not provide the stock holder with any gain. Re-balancing against other securities or against cash converts such a bobbing price into an income stream.

Thanks to a few abusers I am now moderating comments. I welcome thoughtful disagreement. I will block comments with insulting or abusive language. I'm also blocking totally inane comments. Try to make some sense. I am much more likely to allow critical comments if you have the honesty and courage to use your real name.

About Me and This Blog

This is a blog of news, views, and commentary, from a humorous free-market point of view. After one too many rants at the dinner table, my kids called me "the grumpy economist," and hence this blog and its title.
In real life I'm a Senior Fellow of the Hoover Institution at Stanford. I was formerly a professor at the University of Chicago Booth School of Business. I'm also an adjunct scholar of the Cato Institute. I'm not really grumpy by the way!